Cross-border e-commerce data cleaning: detailed explanation of methods and processes

Cross-border e-commerce data cleaning is the process of streamlining the data in the database (removing duplicate recorded data) and converting the remaining data format into a standard and acceptable format. This process not only helps improve data quality, but also lays a solid foundation for subsequent data analysis. This article will discuss in detail the methods and specific processes of cross-border e-commerce data cleaning.

Data cleaning methods

Cleaning incomplete data (i.e. missing values)

In most cases, missing values ​​must be handled by manual filling (i.e. manual cleaning). Of course, some missing values ​​can be derived from this data source or other data sources, which can be used to replace the missing values ​​with average, maximum, minimum, or more complex probability estimates to achieve the purpose of cleaning.

Detection and cleaning of error values

Use statistical analysis methods to identify possible erroneous values ​​or outliers, such as deviation analysis, identifying values ​​that do not follow distributions or regression equations, or use a simple rule base (common sense rules, business-specific rules, etc.) to check the data. Or use constraints between different attributes and external data to detect and clean data.

Detection and cleaning of duplicate records

Records with the same attribute values ​​in the database are considered duplicate records. We detect whether the records are duplicated by judging whether the attribute values ​​​​between the records are equal, and merge the duplicate records into one record (ie merge/clear). Merge/Purge is the basic method of cleaning.

Detection and cleaning of inconsistencies (within and between data sources)

Data integrated from multiple data sources may have semantic conflicts. We can define integrity constraints to detect inconsistencies, and we can also analyze the data to discover the connections between the data to keep the data consistent.

Data cleaning process

Select a subset

Select the columns that require data analysis. When there are many columns in the data, you can use the hiding function to hide the columns that do not need to be analyzed.

Column name renaming

You can change the field name if the original field name is not suitable.

Remove duplicates

Select the range of data you want to analyze and use Excel’s “Remove Duplicates” feature to remove duplicates.

Missing value handling

Select a column in Excel and check the statistics displayed in the lower right corner. By comparing the items in other columns, you can know whether the column is missing.

Conforming processing

Consistency means that the data has a unified naming, and the data can be split to achieve naming consistency.

Data sorting

Use the Functions function in Excel to calculate the average or sum of your data to sort your data.

Exception value viewing and processing

Use Excel’s Filter function to see if there are any errors in your data. In the data types listed in the “Filter” drop-down menu, you can check whether there are any abnormal values.

To sum up, cross-border e-commerce data cleaning is a systematic and complex process. It not only involves data quality issues, but also involves how to effectively manage data. The above is a detailed introduction to the methods and processes of cross-border e-commerce data cleaning.